Bruno Ferman
Biographic Data
| ID | 6369803 |
|---|---|
| NAME | Bruno Ferman |
| GIVEN NAMES | Bruno |
| FAMILY NAME | Ferman |
| SIGNATURE | FERMAN B |
| AFFILIATIONS | Escola de Economia de São Paulo |
| ORCID | 0000-0001-7286-5677 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 21 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
On “Imputation of Counterfactual Outcomes when the Errors are Predictable”
Gonçalves and Ng propose an interesting and simple way to improve counterfactual imputation methods when errors are predictable. For unconditional analyses, this approach yields smaller mean-squared error and tighter prediction intervals in large samples, even if the dependence of the errors is misspecified. For conditional analyses, this approach corrects the bias of standard methods, and provides valid asymptotic inference, if the dependence of…
Inference in difference‐in‐differences
We analyze the challenges for inference in difference‐in‐differences (DID) when there is spatial correlation. We present novel theoretical insights and empirical evidence on the settings in which ignoring spatial correlation should lead to more or less distortions in DID applications. We show that details, such as the time frame used in the estimation, the choice of the treated and control groups, and the choice of the estimator, are key determin…
Skipping the doctor
This paper examines the impact of a policy reform in a municipality in Norway that extended to workers the right to self-certify sickness absence from work. After the reform, workers were no longer obliged to obtain a certificate from a physician to receive sickness benefits. They could call in sick directly to their line leader and had to engage in a counselling program organized by the employer. To estimate the effect of this reform, we contras…
Cherry Picking with Synthetic Controls
We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification‐searching opportunities. We provide theoretical results showing that specification‐searching opportunities are asymptotically irrelevant if we restrict to a subset of SC specifications. However, based on Monte Carlo simulations and simulations with real datasets, we show significant room for specificati…
Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity
We derive an inference method that works in differences-in-differences settings with few treated and many control groups in the presence of heteroskedasticity. As a leading example, we provide theoretical justification and empirical evidence that heteroskedasticity generated by variation in group sizes can invalidate existing inference methods, even in data sets with a large number of observations per group. In contrast, our inference method rema…
Cherry Picking with Synthetic Controls
We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification‐searching opportunities. We provide theoretical results showing that specification‐searching opportunities are asymptotically irrelevant if we restrict to a subset of SC specifications. However, based on Monte Carlo simulations and simulations with real datasets, we show significant room for specificati…
Skipping the doctor
This paper examines the impact of a policy reform in a municipality in Norway that extended to workers the right to self-certify sickness absence from work. After the reform, workers were no longer obliged to obtain a certificate from a physician to receive sickness benefits. They could call in sick directly to their line leader and had to engage in a counselling program organized by the employer. To estimate the effect of this reform, we contras…
Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity
We derive an inference method that works in differences-in-differences settings with few treated and many control groups in the presence of heteroskedasticity. As a leading example, we provide theoretical justification and empirical evidence that heteroskedasticity generated by variation in group sizes can invalidate existing inference methods, even in data sets with a large number of observations per group. In contrast, our inference method rema…
Cherry Picking with Synthetic Controls
We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification‐searching opportunities. We provide theoretical results showing that specification‐searching opportunities are asymptotically irrelevant if we restrict to a subset of SC specifications. However, based on Monte Carlo simulations and simulations with real datasets, we show significant room for specificati…
Skipping the doctor
This paper examines the impact of a policy reform in a municipality in Norway that extended to workers the right to self-certify sickness absence from work. After the reform, workers were no longer obliged to obtain a certificate from a physician to receive sickness benefits. They could call in sick directly to their line leader and had to engage in a counselling program organized by the employer. To estimate the effect of this reform, we contras…
Inference in difference‐in‐differences
We analyze the challenges for inference in difference‐in‐differences (DID) when there is spatial correlation. We present novel theoretical insights and empirical evidence on the settings in which ignoring spatial correlation should lead to more or less distortions in DID applications. We show that details, such as the time frame used in the estimation, the choice of the treated and control groups, and the choice of the estimator, are key determin…
On “Imputation of Counterfactual Outcomes when the Errors are Predictable”
Gonçalves and Ng propose an interesting and simple way to improve counterfactual imputation methods when errors are predictable. For unconditional analyses, this approach yields smaller mean-squared error and tighter prediction intervals in large samples, even if the dependence of the errors is misspecified. For conditional analyses, this approach corrects the bias of standard methods, and provides valid asymptotic inference, if the dependence of…
Computer Science (4 works) · Mathematics (4 works) · Statistics (4 works) · Advanced Causal Inference Techniques (3 works) · Artificial Intelligence (3 works) · Econometrics (3 works) · Statistical Methods and Inference (3 works) · Economics (2 works) · Estimator (2 works) · Inference (2 works)